---
title: "Racial Segregation and Income Disparities"
output: 
  flexdashboard::flex_dashboard:
    orientation: rows
    theme: simplex
    source_code: embed
---


```{r setup, include=FALSE}
library(ggplot2)
library(tidyverse)
library(dplyr)
library(gridExtra)
library(plotly)
library(plyr)
library(flexdashboard)

#working directory
setwd("~/Dropbox/HARRIS/Spring 2018/DATA VIZ/Final paper/Flex")

#Read csv
ethnicity_tidy <- read.csv("ethnicity_tidy.csv")

new_data_ethnicity_percent_tidy <- read.csv("new_data_ethnicity_percent_tidy.csv")

race_tidy <- read.csv("race_tidy.csv")

new_data_race_percent_tidy <- read.csv("new_data_race_percent_tidy.csv")
```



Demographics
=======================================================================

Row
-----------------------------------------------------------------------

### Community population by ethnicity

```{r}
dem_1 <- ethnicity_tidy %>%
  plot_ly(x = ~place, y = ~ ethnicity_tidy$Population, color = ~Ethnicity) %>%
   layout(xaxis = list(
           title = ""), margin = list(b = 70),
         yaxis = list(title = "Population"))


dem_1
```


### Community population by race

```{r}
dem_3 <- race_tidy %>%
  plot_ly(x = ~place, y = ~ race_tidy$Population, color = ~Race) %>%
   layout(xaxis = list(
           title = ""), margin = list(b = 70),
         yaxis = list(title = "Population"))


dem_3
```


Row
-----------------------------------------------------------------------

### Ethnicity distribution within community

```{r}
dem_2 <- new_data_ethnicity_percent_tidy %>%
plot_ly(x = ~Ethnicity, y = ~ new_data_ethnicity_percent_tidy$Percentage, color = ~place )%>%
   layout(
         yaxis = list(title = "Percentage of people"))

dem_2
```


### Race distribution within community
```{r}
dem_4 <- new_data_race_percent_tidy %>%
  plot_ly(x = ~Race, y = ~ new_data_race_percent_tidy$Percent, color = ~place )%>%
   layout(
         yaxis = list(title = "Percentage of people"))



dem_4
```


Income
=======================================================================

### Percentage of HH with income above the median

```{r}
above_median_HH <- read.csv("above_median_per.csv")


above_median_HH$Race <- factor(above_median_HH$Race,
                   levels = c('Asian', 'White', 'All', 'Hispanic', 'Other', 'Black'))

inc_1 <- plot_ly(above_median_HH, x = ~Race, y = ~Percentage, type = 'bar',
        marker = list(color = 'rgba(222,45,38,0.8)',
                      line = list(color = I("black"),
                                  width = 1.5))) %>%
  layout(
       xaxis = list(title = "Race"),
        yaxis = list(title = "Percentage of HH"))


 inc_1
```

### Percentage of HH by income bracket

```{r}
tidy_income_race_pop <- read.csv("tidy_percent_pop_tot2.csv")

tidy_income_race_pop$Income <- factor(tidy_income_race_pop$Income,
                   levels = c('$50,000 to $59,999', '$60,000 to $74,999', '$75,000 to $99,999', '$100,000 to $124,999', '$125,000 to $149,999', '$150,000 to $199,999', '$200,000 or more' ))

inc_2 <- plot_ly(tidy_income_race_pop, x = ~Percentage, y = ~Income, color = ~Race) %>%
  layout(xaxis = list(title = ''), yaxis = list(title = ''), barmode = 'stack', margin = list(l = 150, r = 0, t = 40, b = 5), legend = list(orientation = "h",   
                     xanchor = "center",
                     x = 0.5))
inc_2
```   
    
### Percentage of HH by income bracket

```{r}

tidy_income_hisp <- read.csv("tidy_percent_pop_tot2_Hisp.csv")

tidy_income_hisp$Income <- factor(tidy_income_hisp$Income,
                   levels = c('$50,000 to $59,999', '$60,000 to $74,999', '$75,000 to $99,999', '$100,000 to $124,999', '$125,000 to $149,999', '$150,000 to $199,999', '$200,000 or more' ))

inc_3 <- plot_ly(tidy_income_hisp, x = ~Percentage, y = ~Income, color = ~Race) %>%
  layout(xaxis = list(title = ''), yaxis = list(title = ''), barmode = 'stack', margin = list(l = 150, r = 0, t = 40, b = 5), legend = list(orientation = "h",   
                     xanchor = "center",
                     x = 0.5))
inc_3
```